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license: apache-2.0
tags:
- image-generation
- diffusion
- imagenet
- flow-matching
- self-supervised
datasets:
- imagenet-1k
pipeline_tag: image-to-image
library_name: pytorch
---
# Self-Flow ImageNet 256×256
**Self-Flow** is a self-supervised training method for diffusion transformers that combines flow matching with a self-supervised feature reconstruction objective. This checkpoint is trained on ImageNet 256×256.
### Key Features
- **Architecture**: SiT-XL/2 with per-token timestep conditioning
- **Training**: Flow matching + self-supervised feature reconstruction
- **Resolution**: 256×256 pixels
- **Parameters**: ~675M
## Evaluation Results
| Metric | Value |
|--------|-------|
| FID ↓ | 5.7 |
| IS ↑ | 151.40 |
| sFID ↓ | 4.97 |
| Precision | 0.72 |
| Recall | 0.67 |
*Results computed on 50,000 generated samples vs ImageNet validation set.*
## Usage
### Download Checkpoint
```
python -c "
from huggingface_hub import hf_hub_download
hf_hub_download(
repo_id='Hila/Self-Flow',
filename='selfflow_imagenet256.pt',
local_dir='./checkpoints'
)
print('Downloaded!')
"
```
and follow the instructions in our repository: https://github.com/black-forest-labs/Self-Flow
## License
This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
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